Automatic OpenCL Task Adaptation for Heterogeneous Architectures

International audienceOpenCL defines a common parallel programming language for all devices, although writing tasks adapted to the devices, managing communication and load-balancing issues are left to the programmer. In this work, we propose a novel automatic compiler and runtime technique to execute single OpenCL kernels on heterogeneous multi-device architectures. The technique proposed is completely transparent to the user, does not require off-line training or a performance model. It handles communications and load-balancing issues, resulting from hardware heterogeneity, load imbalance within the kernel itself and load variations between repeated executions of the kernel, in an iterative computation. We present our results on benchmarks and on an N-body application over two platforms, a 12-core CPU with two different GPUs and a 16-core CPU with three homogeneous GPUs

TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation

The paper is concerned with the issue of how software systems actually use Heterogeneous Parallel Architectures (HPAs), with the goal of optimizing power consumption on these resources. It argues the need for novel methods and tools to support software developers aiming to optimise power consumption resulting from designing, developing, deploying and running software on HPAs, while maintaining other quality aspects of software to adequate and agreed levels. To do so, a reference architecture to support energy efficiency at application construction, deployment, and operation is discussed, as well as its implementation and evaluation plans.Comment: Part of the Program Transformation for Programmability in Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March 2016, 7 pages, LaTeX, 3 PNG figure

Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability

Heterogeneous computing, which combines devices with different architectures, is rising in popularity, and promises increased performance combined with reduced energy consumption. OpenCL has been proposed as a standard for programing such systems, and offers functional portability. It does, however, suffer from poor performance portability, code tuned for one device must be re-tuned to achieve good performance on another device. In this paper, we use machine learning-based auto-tuning to address this problem. Benchmarks are run on a random subset of the entire tuning parameter configuration space, and the results are used to build an artificial neural network based model. The model can then be used to find interesting parts of the parameter space for further search. We evaluate our method with different benchmarks, on several devices, including an Intel i7 3770 CPU, an Nvidia K40 GPU and an AMD Radeon HD 7970 GPU. Our model achieves a mean relative error as low as 6.1%, and is able to find configurations as little as 1.3% worse than the global minimum.Comment: This is a pre-print version an article to be published in the Proceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). For personal use onl

An automatic input-sensitive approach for heterogeneous task partitioning

Unleashing the full potential of heterogeneous systems, consisting of multi-core CPUs and GPUs, is a challenging task due to the difference in processing capabilities, memory availability, and communication latencies of different computational resources. In this paper we propose a novel approach that automatically optimizes task partitioning for different (input) problem sizes and different heterogeneous multi-core architectures. We use the Insieme source-to-source compiler to translate a single-device OpenCL program into a multi-device OpenCL program. The Insieme Runtime System then performs dynamic task partitioning based on an offline-generated prediction model. In order to derive the prediction model, we use a machine learning approach based on Artificial Neural Networks (ANN) that incorporates static program features as well as dynamic, input sensitive features. Principal component analysis have been used to further improve the task partitioning. Our approach has been evaluated over a suite of 23 programs and respectively achieves a performance improvement of 22% and 25% compared to an execution of the benchmarks on a single CPU and a single GPU which is equal to 87.5% of the optimal performance

pocl: A Performance-Portable OpenCL Implementation

OpenCL is a standard for parallel programming of heterogeneous systems. The benefits of a common programming standard are clear; multiple vendors can provide support for application descriptions written according to the standard, thus reducing the program porting effort. While the standard brings the obvious benefits of platform portability, the performance portability aspects are largely left to the programmer. The situation is made worse due to multiple proprietary vendor implementations with different characteristics, and, thus, required optimization strategies. In this paper, we propose an OpenCL implementation that is both portable and performance portable. At its core is a kernel compiler that can be used to exploit the data parallelism of OpenCL programs on multiple platforms with different parallel hardware styles. The kernel compiler is modularized to perform target-independent parallel region formation separately from the target-specific parallel mapping of the regions to enable support for various styles of fine-grained parallel resources such as subword SIMD extensions, SIMD datapaths and static multi-issue. Unlike previous similar techniques that work on the source level, the parallel region formation retains the information of the data parallelism using the LLVM IR and its metadata infrastructure. This data can be exploited by the later generic compiler passes for efficient parallelization. The proposed open source implementation of OpenCL is also platform portable, enabling OpenCL on a wide range of architectures, both already commercialized and on those that are still under research. The paper describes how the portability of the implementation is achieved. Our results show that most of the benchmarked applications when compiled using pocl were faster or close to as fast as the best proprietary OpenCL implementation for the platform at hand.Comment: This article was published in 2015; it is now openly accessible via arxi

Optimización del rendimiento y la eficiencia energética en sistemas masivamente paralelos

RESUMEN Los sistemas heterogéneos son cada vez más relevantes, debido a sus capacidades de rendimiento y eficiencia energética, estando presentes en todo tipo de plataformas de cómputo, desde dispositivos embebidos y servidores, hasta nodos HPC de grandes centros de datos. Su complejidad hace que sean habitualmente usados bajo el paradigma de tareas y el modelo de programación host-device. Esto penaliza fuertemente el aprovechamiento de los aceleradores y el consumo energético del sistema, además de dificultar la adaptación de las aplicaciones. La co-ejecución permite que todos los dispositivos cooperen para computar el mismo problema, consumiendo menos tiempo y energía. No obstante, los programadores deben encargarse de toda la gestión de los dispositivos, la distribución de la carga y la portabilidad del código entre sistemas, complicando notablemente su programación. Esta tesis ofrece contribuciones para mejorar el rendimiento y la eficiencia energética en estos sistemas masivamente paralelos. Se realizan propuestas que abordan objetivos generalmente contrapuestos: se mejora la usabilidad y la programabilidad, a la vez que se garantiza una mayor abstracción y extensibilidad del sistema, y al mismo tiempo se aumenta el rendimiento, la escalabilidad y la eficiencia energética. Para ello, se proponen dos motores de ejecución con enfoques completamente distintos. EngineCL, centrado en OpenCL y con una API de alto nivel, favorece la máxima compatibilidad entre todo tipo de dispositivos y proporciona un sistema modular extensible. Su versatilidad permite adaptarlo a entornos para los que no fue concebido, como aplicaciones con ejecuciones restringidas por tiempo o simuladores HPC de dinámica molecular, como el utilizado en un centro de investigación internacional. Considerando las tendencias industriales y enfatizando la aplicabilidad profesional, CoexecutorRuntime proporciona un sistema flexible centrado en C++/SYCL que dota de soporte a la co-ejecución a la tecnología oneAPI. Este runtime acerca a los programadores al dominio del problema, posibilitando la explotación de estrategias dinámicas adaptativas que mejoran la eficiencia en todo tipo de aplicaciones.ABSTRACT Heterogeneous systems are becoming increasingly relevant, due to their performance and energy efficiency capabilities, being present in all types of computing platforms, from embedded devices and servers to HPC nodes in large data centers. Their complexity implies that they are usually used under the task paradigm and the host-device programming model. This strongly penalizes accelerator utilization and system energy consumption, as well as making it difficult to adapt applications. Co-execution allows all devices to simultaneously compute the same problem, cooperating to consume less time and energy. However, programmers must handle all device management, workload distribution and code portability between systems, significantly complicating their programming. This thesis offers contributions to improve performance and energy efficiency in these massively parallel systems. The proposals address the following generally conflicting objectives: usability and programmability are improved, while ensuring enhanced system abstraction and extensibility, and at the same time performance, scalability and energy efficiency are increased. To achieve this, two runtime systems with completely different approaches are proposed. EngineCL, focused on OpenCL and with a high-level API, provides an extensible modular system and favors maximum compatibility between all types of devices. Its versatility allows it to be adapted to environments for which it was not originally designed, including applications with time-constrained executions or molecular dynamics HPC simulators, such as the one used in an international research center. Considering industrial trends and emphasizing professional applicability, CoexecutorRuntime provides a flexible C++/SYCL-based system that provides co-execution support for oneAPI technology. This runtime brings programmers closer to the problem domain, enabling the exploitation of dynamic adaptive strategies that improve efficiency in all types of applications.Funding: This PhD has been supported by the Spanish Ministry of Education (FPU16/03299 grant), the Spanish Science and Technology Commission under contracts TIN2016-76635-C2-2-R and PID2019-105660RB-C22. This work has also been partially supported by the Mont-Blanc 3: European Scalable and Power Efficient HPC Platform based on Low-Power Embedded Technology project (G.A. No. 671697) from the European Union’s Horizon 2020 Research and Innovation Programme (H2020 Programme). Some activities have also been funded by the Spanish Science and Technology Commission under contract TIN2016-81840-REDT (CAPAP-H6 network). The Integration II: Hybrid programming models of Chapter 4 has been partially performed under the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 Programme. In particular, the author gratefully acknowledges the support of the SPMT Department of the High Performance Computing Center Stuttgart (HLRS)

SOCL: An OpenCL Implementation with Automatic Multi-Device Adaptation Support

To fully tap into the potential of today's heterogeneous machines, offloading parts of an application on accelerators is not sufficient. The real challenge is to build systems where the application would permanently spread across the entire machine, that is, where parallel tasks would be dynamically scheduled over the full set of available processing units. In this report we present SOCL, an OpenCL implementation that improves and simplifies the programming experience on heterogeneous architectures. SOCL enables applications to dynamically dispatch computation kernels over processing devices so as to maximize their utilization. OpenCL applications can incrementally make use of light extensions to automatically schedule kernels in a controlled manner on multi-device architectures. A preliminary automatic granularity adaptation extension is also provided. We demonstrate the relevance of our approach by experimenting with several OpenCL applications on a range of representative heterogeneous architectures. We show that performance portability is enhanced by using SOCL extensions.Pour exploiter au mieux les architectures hétérogènes actuelles, il n'est pas suffisant de déléguer aux accélérateurs seulement quelques portions de codes bien déterminées. Le véritable défi consiste à délivrer des applications qui exploitent de façon continue la totalité de l'architecture, c'est-à-dire dont l'ensemble des tâches parallèles les composant sont dynamiquement ordonnancées sur les unités d'exécution disponibles. Dans ce document, nous présentons SOCL, une implémentation de la spécification OpenCL étendue de sorte qu'elle soit plus simple d'utilisation et plus efficace sur les architectures hétérogènes. Cette implémentation peut ordonnancer automatiquement les noyaux de calcul sur les accélérateurs disponibles de façon à maximiser leur utilisation. Les applications utilisant déjà OpenCL peuvent être migrées de façon incrémentale et contrôlée vers SOCL car les extensions fournies sont non intrusives et requièrent très peu de modifications dans les codes. En plus de l'ordonnancement automatique de noyaux de calcul, une extension préliminaire permettant l'adaptation automatique de la granularité est mise à disposition. Nous démontrons la pertinence de cette approche et l'efficacité des extensions fournies à travers plusieurs expérimentations sur diverses architectures hétérogènes représentatives